Medical physics最新文献

{"title":"Biological adaptive radiotherapy for short-time dose compensation in lung SBRT patients.","authors":"Daisuke Kawahara, Akito S Koganezawa, Hikaru Yamaguchi, Takuya Wada, Yuji Murakami","doi":"10.1002/mp.17820","DOIUrl":"https://doi.org/10.1002/mp.17820","url":null,"abstract":"<p><strong>Background: </strong>Conventional adaptive radiation therapy (ART) primarily focuses on adapting to anatomical changes during radiation therapy but does not account for biological effects such as changes in radiosensitivity and tumor response, particularly during treatment interruptions. These interruptions may allow sublethal damage repair in tumor cells, reducing the effectiveness of stereotactic body radiation therapy (SBRT).</p><p><strong>Purpose: </strong>The aim of this study was to develop and evaluate a novel biological adaptive radiotherapy (BART) framework to compensate for the biological effects of radiation interruptions during SBRT for lung cancer.</p><p><strong>Methods: </strong>This study involved lung SBRT patients using volumetric modulated arc therapy. We evaluated the biological dose loss using a microdosimetric kinetic model during four interruption durations (30, 60, 90, and 120 min). The reduction in the biological dose due to interruptions was calculated. The physical dose was calculated from the decreased biological dose in the in-house software, which was incorporated into the TPS. The optimization process was conducted for dose compensation in the TPS. To quantitatively assess the impact of BART on dose distribution, we evaluated the differences in target dose coverage and organ-at-risk (OAR) exposure between the original plan (without interruption), the plan with interruption, the BART plan, and the plan summing the dose before the interruption and the physical dose after compensation (compensated PD plan). The compensated PD plan assumed no biological dose reduction before the interruption.</p><p><strong>Results: </strong>Without BART compensation, interruptions of 30, 60, 90, and 120 min resulted in biological dose reductions, ranging from 12.1% to 19.0% for D<sub>50%</sub> of the gross tumor volume (GTV) and from 16.4% to 24.9% for D<sub>98%</sub> of the PTV. After applying BART, the differences were minimized to -1.5% to -0.6% for D<sub>50%</sub> of the GTV and -0.1% to 0.9% for D<sub>98%</sub> of the PTV. In contrast, the compensated PD plan exhibited larger residual deviations, with dose differences ranging from -9.9% to -14.0% for D<sub>50%</sub> of the GTV and -12.3% to -7.3% for D<sub>98%</sub> of the PTV. The volume differences between the BART plan and the plan without interruption remained within -0.8% to -0.4% for V<sub>5Gy</sub> and -0.2% to 0.0% for V<sub>20Gy</sub>, while differences between the BART and compensated PD plans were similarly small. The maximum dose to the spinal cord (D<sub>0.1cc</sub>) also remained within -0.2 to 0.1 Gy for the BART plan relative to the plan without interruption and -0.1 to -0.5 Gy compared to the compensated PD plan. These results confirm that the OAR doses remained within clinically acceptable constraints across all evaluated plans.</p><p><strong>Conclusion: </strong>This study demonstrated that the BART framework effectively compensates for the biological d","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving pose accuracy and geometry in neural radiance field-based medical image synthesis.","authors":"Twaha Kabika, Cai Hongsen, Zhu Hongling, Dong Jingxian, Zhang Siyuan, Mingyue Ding, Deng Xianbo, Hou Wenguang, Wang Yan","doi":"10.1002/mp.17832","DOIUrl":"https://doi.org/10.1002/mp.17832","url":null,"abstract":"<p><strong>Background: </strong>Neural radiance field (NeRF) models have garnered significant attention for their impressive ability to synthesize high-quality novel scene views from posed 2D images. Recently, the MedNeRF algorithm was developed to render complete computed tomography (CT) projections from a single or a few x-ray images further. Despite this advancement, MedNeRF struggles with accurate pose reconstruction, crucial for radiologists during image analysis, leading to blurry geometry in the generated outputs.</p><p><strong>Purpose: </strong>Motivated by these challenges, our research aims to address MedNeRF's limitations in pose accuracy and image clarity. Specifically, we seek to improve the pose accuracy of reconstructed images and enhance the generated output's anatomical detail and quality.</p><p><strong>Methods: </strong>We propose a novel pose-aware discriminator that estimates pose differences between generated and real patches, ensuring accurate poses and deeper anatomical structures in generated images. We enhance volumetric rendering from single-view x-rays by introducing a customized distortion adaptive loss function and present the HTDataset, a new dataset pair that better mimics machine-generated x-rays, offering clearer anatomical depictions with reduced noise.</p><p><strong>Results: </strong>Our method successfully renders images with correct poses and high fidelity, outperforming existing state-of-the-art methods. The results demonstrate superior performance in both qualitative and quantitative metrics.</p><p><strong>Conclusions: </strong>The proposed approach addresses the pose reconstruction challenge in MedNeRF, enhances the anatomical detail, and reduces noise in generated images. The use of HTDataset and the innovative discriminator structure lead to significant improvements in the accuracy and quality of the rendered images, setting a new benchmark in the field.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model observer task-based assessment of computed tomography metal artifact reduction using a hip arthroplasty phantom.","authors":"Grant Fong, Steven Izen, Andrew Primak, Nancy Obuchowski, Wadih Karim, Brian Herts, Naveen Subhas","doi":"10.1002/mp.17817","DOIUrl":"https://doi.org/10.1002/mp.17817","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The United States Food and Drug Administration (FDA) recently published a model observer-based framework for the objective performance assessment of computed tomography (CT) metal artifact reduction (MAR) algorithms and demonstrated the framework's feasibility in the low-contrast detectability (LCD) task-based assessment of MAR performance in a mathematical phantom.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study investigates the feasibility of the model observer-based framework in LCD task-based assessment of MAR performance using a physical arthroplasty phantom, results of which were then compared with the performance of human observers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A phantom simulating a unilateral hip prosthesis was designed with a rotatable insert containing a metal implant (cobalt-chromium spheres attached to titanium rods) and 16 unique low-contrast spherical lesions. Each lesion was scanned 100 times on a CT scanner (Somatom Force, Siemens Healthineers) with standard full-dose and half-dose protocols (140 kVp, 300 and 150 quality reference mAs) in each of four different insert rotations to supply 100 pairs of signal-present (lesion) and signal-absent (background) images needed for model observer analyses. Lesion detectability (d') using channelized Hotelling observers (CHO) was optimized by testing different image transformation techniques and channel selection (Gabor and Laguerre-Gauss [LG]) and calculated for each lesion reconstructed with and without iterative MAR (iMAR, Siemens Healthineers). Linear regression was used to assess the d' in each image set. Spearman's correlation was used to compare d' results to human detectability and confidence scores from a previously published human observer study involving the same phantom.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;CHO d' measurements using LG channels were less sensitive to artifacts than those using Gabor channels and were therefore selected for the LCD assessment. Image masking and thresholding provided more accurate d' by isolating the signal and minimizing background differences. For all lesions, d' values of full-dose iMAR images were significantly greater than those of filtered back projection (FBP) images at full dose (p &lt; 0.001) and half dose (p &lt; 0.001). Additionally, d' values of half-dose iMAR images were significantly greater than those of FBP images at full dose (p = 0.010) and half dose (p &lt; 0.001). The d' values were not significantly different between full-dose and half-dose FBP (p = 0.620) or between full-dose and half-dose iMAR (p = 0.358). Pooling across all lesions, d' measurements were positively correlated with human detection rate (Spearman correlation coefficient = 0.723; p &lt; 0.001) and confidence scores (Spearman correlation coefficient = 0.727; p &lt; 0.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The use of CHO in the LCD assessment of MAR performance can be feasibly performed on a physical phantom, and results using this method correlated ","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing cardiac MRI multi-structure segmentation: A semi-supervised multidimensional consistency constraint learning network.","authors":"Hongzhen Cui, Meihua Piao, Xinghe Huang, Xiaoyue Zhu, Haoming Ma, Yunfeng Peng","doi":"10.1002/mp.17805","DOIUrl":"https://doi.org/10.1002/mp.17805","url":null,"abstract":"<p><strong>Background: </strong>Deep convolutional neural networks (DCNNs) have been proposed for medical Magnetic Resonance Imaging (MRI) segmentation, but their effectiveness is often limited by challenges in semantic discrimination, boundary delineation, and spatial context modeling.</p><p><strong>Purpose: </strong>To address these challenges, we present the Multidimensional Consistency Constraint Learning Network (MDCC-Net) for multi-structure segmentation of cardiac MRI using a semi-supervised approach.</p><p><strong>Methods: </strong>MDCC-Net incorporates a shared encoder, multiple differentiated decoders, and leverages pyramid boundary consistency features and spatial consistency constraints. The model employs mutual consistency constraints and pseudo-labels to enhance segmentation performance. Additionally, MDCC-Net uses a combination of Dice loss and mean squared error loss to facilitate convergence and improve accuracy.</p><p><strong>Results: </strong>Experiments on the ACDC cardiac MRI dataset demonstrate that MDCC-Net achieves state-of-the-art performance in multi-structure segmentation of the left ventricle (LV), myocardium (MYO), and right ventricle (RV). Specifically, MDCC-Net attained a Dice coefficient (Dice) of 0.8763 and a Jaccard index of 0.7906 on average. The right ventricle's Average Surface Distance (ASD) reached a best performance of 0.5391, and the left ventricle's Dice attained an optimal value of 0.8965. These results highlight the model's superior ability to utilize semi-supervised data through consistency and entropy minimization constraints. In addition, the generalization of MDCC-Net is verified on the M&Ms dataset.</p><p><strong>Conclusions: </strong>MDCC-Net significantly enhances the multi-structure segmentation of cardiac MRI under multidimensional consistency constraints. This approach provides a foundational study for integrating multifeature fusion in clinical automated and semiautomated multi-organ and multi-tissue segmentation, thus potentially improving diagnostic and treatment planning processes in clinical settings.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of residual setup errors in head and neck patients from upright versus supine radiotherapy postures.","authors":"Jiayao Sun, Lijia Zhang, Weiwei Wang, Lin Kong, Xiyin Guan, Sixue Dong, Dan You, Zhuangming Shen, Yinxiangzi Sheng","doi":"10.1002/mp.17824","DOIUrl":"https://doi.org/10.1002/mp.17824","url":null,"abstract":"<p><strong>Background: </strong>Carbon-ion rotating gantries use is limited by its large size, weight, and high cost. Gantry-free modality enables the reduction of the overall size, weight, and cost. Among them, upright treatment, which utilizes fixed ion beamlines, in combination with a treatment chair capable of 360° rotation and adjustable pitch angle (enabling non-coplanar beam delivery), provides a wider range of beam entry angles compared to conventional couch-based setups and has already been applied in particle radiotherapy for head and neck cancer patients.</p><p><strong>Purpose: </strong>In this study, we analyzed clinical data from the Shanghai Proton and Heavy Ion Center (SPHIC) to quantify residual setup errors across various regions of interest (ROIs) for both upright and supine treatments.</p><p><strong>Methods: </strong>A total of 402 treatment fractions from 28 patients (median 5 fractions, range: 5-16 fractions per posture per patient) were enrolled in this study. All these patients were immobilized and scanned in supine posture and received both supine and upright radiotherapy. Three rectangular-shaped ROIs were delineated based on bone structures, encompassing the mandible, orbit, and neck vertebrae C1-C3. Box-based registration, focusing solely on the anatomical structures within the specific ROIs was performed to subtract the correction vector used in treatment, thereby obtaining the residual setup error for each ROI. Margins for each ROIs were calculated.</p><p><strong>Results: </strong>For both postures, the median values of residual setup error for all translational directions were less than 1 mm. The median values did not exceed 0.2 degrees for rotational errors. More than 78% of the fractions for upright treatment fell within the 1 mm/° threshold, while 94% were within the 2 mm/° threshold. In contrast, for supine treatment, over 61% fell within the 1 mm/° threshold, while 86% were within the 2 mm/° threshold. The maximum margin was 3.3 mm in the AP direction of the C1-C3 region for the supine posture.</p><p><strong>Conclusions: </strong>Upright treatments demonstrated comparable residual setup errors to supine treatments, with most errors falling within clinically acceptable thresholds. This study provides valuable clinical evidence for the continued development and implementation of upright radiotherapy.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving decomposition image quality in dual-energy chest radiography using two-dimensional crisscrossed anti-scatter grid.","authors":"Duhee Jeon, Younghwan Lim, Hyesun Yang, Myeongkyu Park, Kyong-Woo Kim, Hyosung Cho","doi":"10.1002/mp.17819","DOIUrl":"https://doi.org/10.1002/mp.17819","url":null,"abstract":"<p><strong>Background: </strong>Chest radiography is a widely used medical imaging modality for diagnosing chest-related diseases. However, anatomical structure overlap hinders accurate lesion detection. While the dual-energy x-ray imaging technique addresses this issue by separating soft-tissue and bone images from an original chest radiograph, scattered radiation remains a significant challenge in decomposition image quality.</p><p><strong>Purpose: </strong>This work aims to conduct dual-energy material decomposition (DEMD) in chest radiography using a two-dimensional (2D) crisscrossed anti-scatter grid to improve decomposition image quality by effectively removing scattered radiation.</p><p><strong>Methods: </strong>A 2D graphite-interspaced grid with a strip density of N = 1.724 lines/mm and grid ratio r = 6:1 was fabricated using a high-precision sawing process. The grid characteristics were evaluated using the IEC standard fixture. A 2D-grid-based DEMD process, which involves the acquisition of low- and high-kV radiographs with a 2D grid, generation of a pairwise decomposition function using a calibration wedge phantom, and decomposition of soft-tissue and bone images using the decomposition function, was implemented, followed by software-based grid artifact reduction. Experiments were conducted on a commercially available chest phantom using an x-ray imaging system operating at two tube voltages of 70 and 120 kVp. The decomposition image quality of the proposed DEMD and conventional dual-energy subtraction methods was compared for the cases of no grid, software-based scatter correction, 1D grid (N = 8.475 lines/mm and r = 12:1), and 2D grid.</p><p><strong>Results: </strong>The 2D grid demonstrated superior scatter radiation removal ability with scatter radiation transmission of 6.34% and grid selectivity of 9.67, representing a 2.6-fold decrease and a 2.7-fold improvement over the 1D grid, respectively. Compared to other competitive methods, the 2D-grid-based DEMD method considerably improved decomposition image quality, with improved lung structure visibility in selective soft-tissue images.</p><p><strong>Conclusions: </strong>The proposed DEMD method yielded high-quality dual-energy chest radiographs by effectively removing scattered radiation, demonstrating significant potential for improving lesion detection in clinical practice.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative evidential synthesis with integrated segmentation framework for MR-only radiation therapy treatment planning.","authors":"Lina Mekki, Matthew Ladra, Sahaja Acharya, Junghoon Lee","doi":"10.1002/mp.17828","DOIUrl":"https://doi.org/10.1002/mp.17828","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Radiation therapy (RT) planning is a time-consuming process involving the contouring of target volumes and organs at risk, followed by treatment plan optimization. CT is typically used as the primary planning image modality as it provides electron density information needed for dose calculation. MRI is widely used for contouring after registration to CT due to its high soft tissue contrast. However, there exists uncertainties in registration, which propagate throughout treatment planning as contouring errors, and lead to dose inaccuracies. MR-only RT planning has been proposed as a solution to eliminate the need for CT scan and image registration, by synthesizing CT from MRI. A challenge in deploying MR-only planning in clinic is the lack of a method to estimate the reliability of a synthetic CT in the absence of ground truth. While methods have used sampling-based approaches to estimate model uncertainty over multiple inferences, such methods suffer from long run time and are therefore inconvenient for clinical use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To develop a fast and robust method for the joint synthesis of CT from MRI, estimation of model uncertainty related to the synthesis accuracy, and segmentation of organs at risk (OARs), in a single model inference.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this work, deep evidential regression is applied to MR-only brain RT planning. The proposed framework uses a multi-task vision transformer combining a single joint nested encoder with two distinct convolutional decoder paths for synthesis and segmentation separately. An evidential layer was added at the end of the synthesis decoder to jointly estimate model uncertainty in a single inference. The framework was trained and tested on a dataset of 119 (80 for training, 9 for validation, and 30 for test) paired T1-weighted MRI and CT scans with OARs contours.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The proposed method achieved mean ± SD SSIM of 0.820 ± 0.039, MAE of 47.4 ± 8.49 HU, and PSNR of 23.4 ± 1.13 for the synthesis task and dice similarity coefficient of 0.799 ± 0.132 (lenses), 0.945 ± 0.020 (eyes), 0.834 ± 0.059 (optic nerves), 0.679 ± 0.148 (chiasm), 0.947 ± 0.014 (temporal lobes), 0.849 ± 0.027 (hippocampus), 0.953 ± 0.024 (brainstem), 0.752 ± 0.228 (cochleae) for segmentation-in a total run time of 6.71 ± 0.25 s. Additionally, experiments on challenging test cases revealed that the proposed evidential uncertainty estimation highlighted the same uncertain regions as Monte Carlo-based epistemic uncertainty, thus highlighting the reliability of the proposed method.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;A framework leveraging deep evidential regression to jointly synthesize CT from MRI, predict the related synthesis uncertainty, and segment OARs in a single model inference was developed. The proposed approach has the potential to streamline the planning process and provide clinicians with a measure of the reliability of a synthetic C","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interplay-robust optimization for treating irregularly breathing lung patients with pencil beam scanning.","authors":"Ivar Bengtsson, Anders Forsgren, Albin Fredriksson, Ye Zhang","doi":"10.1002/mp.17821","DOIUrl":"https://doi.org/10.1002/mp.17821","url":null,"abstract":"<p><strong>Background: </strong>The steep dose gradients obtained with pencil beam scanning allow for precise targeting of the tumor but come at the cost of high sensitivity to uncertainties. Robust optimization is commonly applied to mitigate uncertainties in density and patient setup, while its application to motion management, called 4D-robust optimization (4DRO), is typically accompanied by other techniques, including gating, breath-hold, and re-scanning. In particular, current commercial implementations of 4DRO do not model the interplay effect between the delivery time structure and the patient's motion.</p><p><strong>Purpose: </strong>Interplay-robust optimization (IPRO) has previously been proposed to explicitly model the interplay-affected dose during treatment planning. It has been demonstrated that IPRO can mitigate the interplay effect given the uncertainty in the patient's breathing frequency. In this study, we investigate and evaluate IPRO in the context where the motion uncertainty is extended to also include variations in breathing amplitude.</p><p><strong>Methods: </strong>The compared optimization methods are applied and evaluated on a set of lung patients. We model the patients' motion using synthetic 4D computed tomography (s4DCT), each created by deforming a reference CT based on a motion pattern obtained with 4D magnetic resonance imaging. Each (s4DCT) contains multiple breathing cycles, partitioned into two sets for scenario generation: one for optimization and one for evaluation. Distinct patient motion scenarios are then created by randomly concatenating breathing cycles varying in period and amplitude. In addition, a method considering a single breathing cycle for generating optimization scenarios (IPRO-1C) is developed to investigate to which extent robustness can be achieved with limited information. Both IPRO and IPRO-1C were investigated with 9, 25, and 49 scenarios.</p><p><strong>Results: </strong>For all patient cases, IPRO and IPRO-1C increased the target coverage in terms of the near-worst-case (5th percentile) CTV D98, compared to 4DRO. After normalization of plan doses to equal target coverage, IPRO with 49 scenarios resulted in the greatest decreases in OAR dose, with near-worst-case (95th percentile) improvements averaging 4.2 %. IPRO-1C with 9 scenarios, with comparable computational demands as 4DRO, decreased OAR dose by 1.7 %.</p><p><strong>Conclusions: </strong>The use of IPRO could lead to more efficient mitigation of the interplay effect, even when based on the information from a single breathing cycle. This can potentially decrease the need for real-time motion management techniques that prolong treatment times and decrease patient comfort.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-supervised medical image segmentation based on dual swap data mixing and cross EMA strategies.","authors":"Licheng Zheng, Lihui Wang, Yingfeng Ou, Li Wang, Caiqing Jian, Yuemin Zhu","doi":"10.1002/mp.17809","DOIUrl":"https://doi.org/10.1002/mp.17809","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Semi-supervised medical image segmentation methods based on mean teacher (MT) framework provide a promising means for addressing the dense prediction problems with limited annotated images and numerous unlabeled images. However, the confirmation bias caused by the distribution difference between labeled and unlabeled data and the parameters-coupling problem of MT prevent the model from further improving the segmentation performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To reduce confirmation bias and alleviate the parameter coupling problem in MT framework, a novel data augmentation strategy and a cross exponential moving averaging (crossEMA) architecture are proposed in this work.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Specifically, a dual swap mixing data augmentation method was first proposed, which exchanges the patches between labeled and unlabeled images twice to decrease the confirmation bias caused by distribution divergency. Subsequently, a novel architecture for both student and teacher networks was designed with structurally identical dual decoders, one of which adopted a dropout operation. Labeled, unlabeled, and mixed images are fed into this MT architecture. For unlabeled data, the pseudo-labels generated by the dual decoders of the teacher network were used to supervise the predictions of the corresponding decoders of the student network. For mixed data, the real labels of the labeled data are mixed with the pseudo-labels of the unlabeled data predicted by the teacher network to form the supervisory information, which is used to constrain the prediction consistency for mixed data between student and teacher networks. To overcome the parameter coupling problem between the student and teacher networks, the encoder parameters of the teacher network were updated using an exponential moving average (EMA) strategy, while its dual decoder parameters were updated using a cross EMA strategy, which means the perturbed decoder parameters of the student network were updated with the non-perturbed decoder parameters of the student network and vice versa.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;By comparing with several state-of-the-art (SOTA) semi-supervised segmentation methods on four publicly available datasets, we validated that the proposed method outperforms existing models. The Dice similarity coefficient (DSC) and volume similarity (VS) were improved by at least 2.33% and 1.86%, respectively, compared to the corresponding sub-optimal methods. Through multiple ablation experiments, we verified that the proposed dual swap strategy can reduce the distributional differences between unlabeled data and labeled+mixed data. In addition, the cross EMA strategy can avoid early convergence of the student and teacher networks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The proposed strategies can alleviate the confirmation bias caused by the distribution discrepancy between labeled and unlabeled data in semi-supervised learning, as well as the","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast Cancer: Habitat imaging based on intravoxel incoherent motion for predicting pathologic complete response to neoadjuvant chemotherapy.","authors":"Hui Zhang, Yunyan Zheng, Mingzhe Zhang, Ailing Wang, Yang Song, Chenglong Wang, Guang Yang, Mingping Ma, Muzhen He","doi":"10.1002/mp.17813","DOIUrl":"https://doi.org/10.1002/mp.17813","url":null,"abstract":"<p><strong>Background: </strong>Radiomics research based on whole tumors is limited by the unclear biological significance of radiomics features, which therefore lack clinical interpretability.</p><p><strong>Purpose: </strong>We aimed to determine whether features extracted from subregions defined by habitat imaging, reflecting tumor heterogeneity, could identify breast cancer patients who will benefit from neoadjuvant chemotherapy (NAC), to optimize treatment.</p><p><strong>Methods: </strong>143 women with stage II-III breast cancer were divided into a training set (100 patients, 36 with pathologic complete response [pCR]) and a test set (43 patients, 16 with pCR). Patients underwent 3-T magnetic resonance imaging (MRI) before NAC. With the pathological results as the gold standard, we used the training set to build models for predicting pCR based on whole-tumor radiomics (Model_WH), intravoxel incoherent motion (IVIM)-based habitat imaging (Model_Habitats), conventional MRI features (Model_CF), and immunohistochemical findings (Model_IHC). We also built the combined models Model_Habitats+CF and Model_{Habitats+CF+IHC}. In the test set, we compared the performance of the combined models with that of the invasive Model_IHC by using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of the model. The DeLong test was used to compare diagnostic efficiency across different parameters.</p><p><strong>Results: </strong>In the prediction of pCR, Model_WH, Model_Habitats, Model_CF, Model_IHC, Model_Habitats+CF, Model_CF+IHC and Model_{Habitats+CF+IHC} achieved AUCs of 0.895, 0.757, 0.705, 0.807, 0.800, 0.856, and 0.891 respectively, in the training set and 0.549, 0.708, 0.700, 0.788, 0.745, 0.909, and 0.891 respectively, in the test set. The DeLong test revealed no significant difference between Model_IHC versus Model_Habitats+CF (p = 0.695) and Model_{Habitats+CF+IHC} versus Model_CF+IHC (p = 0.382) but showed a significant difference between Model_IHC and Model_{Habitats+CF+IHC} (p = 0.043).</p><p><strong>Conclusion: </strong>The habitat model we established from first-order features combined with conventional MRI features and IHC findings accurately predicted pCR before NAC. This model can facilitate decision-making during individualized treatment for breast cancer.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}